Algorithmic Enhancement of Brain-Computer Interface Control: Harnessing Thought for Precise System Management

Brain-computer interface (BCI) control technology is a cutting-edge technology that allows users to control computers, mechanical devices, and other electronic devices with their minds by directly connecting the brain to external devices. This manuscript examines the methodologies employed in signal acquisition and decoding. A comparative analysis is conducted between two distinct approaches to signal acquisition: invasive and non-invasive techniques. The discussion encompasses their respective advantages, limitations, and contextual applications. Then, this paper cross-sectionally compares the application of several different machine learning (ML) algorithms in BCI control awareness, such as Autoregressive (AR), Long Short-Term Memory (LSTM) in Temporal Model; Support Vector Machines (SVM), Neural Network (NN), Ridge regression (RR) in Regression model. All these algorithms play an important role in signal decoding for BCI. Finally, this paper successfully decodes and analyzes the signals using the Hidden Markov Algorithm (HMM) as an example, which will show some value for future applications in various fields such as consciousness control and medical rehabilitation.